Communications of Mathematical Education (한국수학교육학회지시리즈E:수학교육논문집)

Korean Society of Mathematical Education (한국수학교육학회)
권호 표지
DOI QR코드

DOI QR Code

Development of an impact Identification Program in Mathematical Education Research Using Machine Learning and Network

기계학습과 네트워크를 이용한 수학교육 연구의 영향력 판별 프로그램 개발

  • 오세준 (이화여자대학교 사범대학부속 이화.금란고등학교) ;
  • 권오남 (서울대학교)
  • Received : 2023.02.24
  • Accepted : 2023.03.14
  • Published : 2023.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

This study presents a machine learning program designed to identify impactful papers in the field of mathematics education. To achieve this objective, we examined the impact of papers from a scientific econometrics perspective, developed a mathematics education research network, and defined the impact of mathematics education research using PageRank, a network centrality index. We developed a machine learning model to determine the impact of mathematics education research and identified the journals with the highest percentage of impactful articles to be the Journal for Research in Mathematics Education (25.66%), Educational Studies in Mathematics (22.12%), Zentralblatt für Didaktik der Mathematik (8.46%), Journal of Mathematics Teacher Education (5.8%), and Journal of Mathematical Behaviour (5.51%). The results of the machine learning program were similar to the findings of previous studies that were read and evaluated qualitatively by experts in mathematics education. Significantly, the AI-assisted impact evaluation of mathematics education research, which typically requires significant human resources and time, was carried out efficiently in this study.

본 연구는 수학교육에서 영향력 있는 논문을 판별하는 기계학습 프로그램 개발 연구이다. 이를 위하여 과학계량학의 관점에서 논문의 영향력을 조명하고, 수학교육 연구 네트워크를 구성하고, 네트워크 중심성 지수인 PageRank로 수학교육 연구의 영향력으로 정의하였다. 영향력 있는 수학교육 연구를 판별하기 위하여 기계학습 모델을 설계하였으며, 이를 이용하여 영향력 있는 논문이 게재된 비율이 높은 학술지를 조사한 결과 Journal for Research in Mathematics Education(25.66%), Educational Studies in Mathematics(22.12%), Zentralblatt für Didaktik der Mathematik(8.46%), Journal of Mathematics Teacher Education(5.8%), Journal of Mathematical Behavior(5.51%) 순으로 나타났다. 수학교육 전문가들이 직접 논문을 읽고 질적으로 평가한 선행연구 결과와 유사한 결과를 기계학습 프로그램으로 도출할 수 있었다. 많은 인원과 시간이 필요했던 수학교육 연구의 영향력 평가를 인공지능을 이용하여 효율적으로 실시할 수 있었다는 점에서 의의가 있다.

Keywords

References

  1. Kwon, S. (2014). A study on the factors influencing semantic relation in building a structured glossary. Journal of the Korean Society for Library and Information Science, 48(2), 353-378.  https://doi.org/10.4275/KSLIS.2014.48.2.353
  2. Kwon, O., & Ju, M. (2003). Research Trends and Tasks in Collegiate Mathematics Education. The Mathematical Education, 42(2), 229-245. 
  3. Kim, P., & Lee, J. (2010). A study on journal impact measurement with hirsch-type indices. Journal of the Korean Society for Information Management, 27(1), 269-287.  https://doi.org/10.3743/KOSIM.2010.27.1.269
  4. Moon, S., & Kim, I. (2018). A study of the relationship between centrality and research performance in collaborative research network. KIPS Transactions on Software and Data Engineering, 7(5), 169-176.  https://doi.org/10.3745/KTSDE.2018.7.5.169
  5. Park, K. (2003). An Analysis of the Secondary Mathematics Education Research Trends in Korea by Comparing the Papers on and Those on. The Mathematical Education, 42(2), 219-228. 
  6. Park, S., & Kim, W. (2011). A comparative analysis on research trends of secondary mathematics education between Korea and overseas. The Mathematical Education, 50(3), 285-308.  https://doi.org/10.7468/MATHEDU.2011.50.3.285
  7. Pang, J., Jin, W., Cho, S., Lee, Y., Kim, E., Kim, Y., Park, Y., Kim, K., Hwang, J., & Lee, H.(2019). Domestic research trends of mathematics education: An analysis of journals published from 1963 to 2019. Journal of Educational Research in Mathematics, 29(4), 709-739. 
  8. Ahn, D., & Lee, K. (2022). Analysis of achievement predictive factors and predictive AI model development - Focused on blended math classes. The Mathematical Education, 61(2), 257-271.  https://doi.org/10.7468/MATHEDU.2022.61.2.257
  9. Lee, J. (2008). Analyzing the network of academic disciplines with journal contributions of Korean researchers. Journal of the Korean Society for Information Management, 25(4), 327-345.  https://doi.org/10.3743/KOSIM.2008.25.4.327
  10. Lee, J. (2011). Journal pagerank calculation in the Korean science citation database. Journal of the Korean BIBLIA Society for Library and Information Science, 22(4), 361-379. 
  11. Lee, J. (2014). A comparative study on the centrality measures for analyzing research collaboration networks. Journal of the Korean Society for Information Management, 31(3), 153-179.  https://doi.org/10.3743/KOSIM.2014.31.3.153
  12. Lee, J., Lee, J., Kang, I., Shin, S., & Jung, H. (2007). Automatic recommendation of panel pool using a probabilistic ontology and researcher networks. Journal of the Korean Society for Information Management, 24(3), 43-65.  https://doi.org/10.3743/KOSIM.2007.24.3.043
  13. Lee, J., & Cho. K. (2021). A study on the prediction model for the ratio of mathematics low-performing students in middle school using machine learning. Journal of Educational Technology, 37(1), 95-129.  https://doi.org/10.17232/KSET.37.1.095
  14. Cho E., & Song J. (2013). 어떤 국내 논문이 더 많이 인용되는가?. Korean Journal of Marketing, 28(4), 33-56.
  15. Acuna, D. E., Allesina, S., & Kording, K. P. (2012). Predicting scientific success. Nature, 489(7415), 201-202. https://doi.org/10.1038/489201a
  16. Aljuaid, H., Iftikhar, R., Ahmad, S., Asif, M., & Afzal, M. T. (2021). Important citation identification using sentiment analysis of in-text citations. Telematics and Informatics, 56, 101492.
  17. Altuntas, S., Dereli, T., & Kusiak, A. (2015). Forecasting technology success based on patent data. Technological Forecasting and Social Change, 96, 202-214. https://doi.org/10.1016/j.techfore.2015.03.011
  18. Andrade-Molina, M., Montecino, A., & Aguilar, M. S. (2020). Beyond quality metrics: defying journal rankings as the philosopher's stone of mathematics education research. Educational Studies in Mathematics, 103(3), 359-374.
  19. Barnes, J. A., & Harary, F. (1983). Graph theory in network analysis. Social networks, 5(2), 235-244. https://doi.org/10.1016/0378-8733(83)90026-6
  20. Bildosola, I., Rio-Belver, R. M., Garechana, G., & Cilleruelo, E. (2017). TeknoRoadmap, an approach for depicting emerging technologies. Technological Forecasting and Social Change, 117, 25-37 https://doi.org/10.1016/j.techfore.2017.01.015
  21. Bollen, J., Rodriquez, M. A., & Van de Sompel, H. (2006). Journal status. Scientometrics, 69(3), 669-687. https://doi.org/10.1007/s11192-006-0176-z
  22. Bornmann, L., Mutz, R., Neuhaus, C., & Daniel, H. D. (2008). Citation counts for research evaluation: standards of good practice for analyzing bibliometric data and presenting and interpreting results. Ethics in science and environmental politics, 8(1), 93-102. https://doi.org/10.3354/esep00084
  23. Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems, 30(1-7), 107-117. https://doi.org/10.1016/S0169-7552(98)00110-X
  24. Chen, P., Xie, H., Maslov, S., & Redner, S. (2007). Finding scientific gems with Google's PageRank algorithm. J ournal of Informetrics, 1(1), 8-15. https://doi.org/10.1016/j.joi.2006.06.001
  25. Ding, Y., & Cronin, B. (2011). Popular and/or prestigious? Measures of scholarly esteem. Information Processing & Management, 47(1), 80-96.
  26. Dong, Y., Johnson, R. A., & Chawla, N. V. (2015, February). Will this paper increase your h-index? Scientific impact prediction. In Proceedings of the eighth ACM international conference on web search and data mining (pp. 149-158).
  27. English, L. D., & Kirshner, D. (Eds.). (2002). Handbook of international research in mathematics education. Lawrence Erlbaum.
  28. Forgasz, H. J. (2019). Scholarly writing. In Compendium for Early Career Researchers in Mathematics Education, 359-374. Springer.
  29. Fujigaki, Y. (1998). Filling the gap between discussions on science and scientists' everyday activities: Applying the autopoiesis system theory to scientific knowledge. Social Science Information, 37(1), 5-22. https://doi.org/10.1177/053901898037001001
  30. Funk, R. J., & Owen-Smith, J. (2017). A dynamic network measure of technological change. Management science, 63(3), 791-817.
  31. Garfield, E., & Sher, I. H. (1963). New factors in the evaluation of scientific literature through citation indexing. American documentation, 14(3), 195-201. https://doi.org/10.1002/asi.5090140304
  32. Garfield, E. (2009). From the science of science to Scientometrics visualizing the history of science with HistCite software. Journal of Informetrics, 3(3), 173-179. https://doi.org/10.1016/j.joi.2009.03.009
  33. Grouws, D. (Ed.). (2006). Handbook of research on mathematics teaching and learning:(A project of the national council of teachers of mathematics). IAP.
  34. Grouws, D. A. (1988). Perspectives on Research on Effective Mathematics Teaching. Volume 1. National Council of Teachers of Mathematics, 1906 Association Drive, Reston, VA 22091.
  35. Grouws, D. A. (Ed.). (1992). Handbook of research on mathematics teaching and learning. MacMillan.
  36. Haensly, P. J., Hodges, P. E., & Davenport, S. A. (2008). Acceptance rates and journal quality: An analysis of economics and finance. J ournal of Business & Finance Librarianship, 14(1), 2-31. https://doi.org/10.1080/08963560802176330
  37. Hirsch, J. E. (2005). An index to quantify an individual's scientific research output. Proceedings of the National academy of Sciences, 102(46), 16569-16572. https://doi.org/10.1073/pnas.0507655102
  38. Hood, W., & Wilson, C. (2001). The literature of bibliometrics, scientometrics, and informetrics. Scientometrics, 52(2), 291-314. https://doi.org/10.1023/A:1017919924342
  39. Inglis, M., & Foster, C. (2018). Five decades of mathematics education research. Journal for Research in Mathematics Education, 49(4), 462-500.
  40. Johnson, W. R. (1987). Empowering practitioners: Holmes, Carnegie, and the lessons of history. History of Education Quarterly, 27(2), 221-240 https://doi.org/10.2307/368471
  41. Lakatos, I. (1978). The methodology of scientific research programmes: Philosophical papers Volume Cambridge, United Kingdom: Cambridge University Press. doi:10.1017/CBO9780511621123.
  42. Lee, H. J., & Yang, H. Y. (2015). 시간적 효과가 고려된 PageRank 함수를 이용한 핵심특허 탐색방법. In Proce dings of the Korea Technology Innovation Society Conference, 275-281.
  43. Lester, F. K. (2005). On the theoretical, conceptual, and philosophical foundations for research in mathematics education. ZDM, 37(6), 457-467. doi:10.1007/BF02655854
  44. Mariani, M. S., Medo, M., & Zhang, Y. C. (2016). Identification of milestone papers through time-balanced network centrality. Journal of Informetrics, 10(4), 1207-1223. https://doi.org/10.1016/j.joi.2016.10.005
  45. Merton, R. C. (1973). An intertemporal capital asset pricing model. Econometrica; J ournal of the Econometric Society, 867-887.
  46. Nalimov, V. V., & Mulchenko, Z. M. (1971). Measurement of Science. Study of the Development of Science as an Information Process.
  47. Neylon, C., & Wu, S. (2009). level metrics and the evolution of scientific impact. PLoS biology, 7(11), e1000242.
  48. Nivens, R. A., & Otten, S. (2017). Assessing journal quality in mathematics education. Journal for Research inMathematics Education, 48(4), 348-368.
  49. Presmeg, N., & Kilpatrick, J. (2019). Pleasures, power, and pitfalls of writing up mathematics education research. Compendium for early career researchers in mathematics education, 347-358.
  50. Price, D. (1963). Little science, big science... and beyond (Vol. 480). New York: Columbia University Press.
  51. Price, D. (1976). A general theory of bibliometric and other cumulative advantage processes. Journal of the American society for Information Science, 27(5), 292-306. https://doi.org/10.1002/asi.4630270505
  52. Schoenfeld, A. H. (2007). Method. In F. K. Lester (Ed.), Second handbook of research on mathematics teaching and learning (Vol. 1, pp. 69-107). Information Age
  53. Seglen, P. O. (1997). Why the impact factor of journals should not be used for evaluating research. Bmj, 314(7079), 497.
  54. Sierpinska, A., & Kilpatrick, J. (1998). The ICMI Study Conference. In Mathematics Education as a Research Domain: A Search for Identity (pp. 3-32). Springer.
  55. Walker, D., Xie, H., Yan, K. K., & Maslov, S. (2007). Ranking scientific publications using a model of network traffic. Journal of Statistical Mechanics: Theory and Experiment, 2007(06), P06010.
  56. Waltman, L., & Yan, E. (2014). PageRank-related methods for analyzing citation networks. In Measuring scholarly impact (pp. 83-100). Springer.
  57. Weihs, L., & Etzioni, O. (2017, June). Learning to predict citation-based impact measures. In 2017 ACM/IEEE joint conference on digital libraries (JCDL) (pp. 1-10). IEEE.
  58. Weis, J. W., & Jacobson, J. M. (2021). Learning on knowledge graph dynamics provides an early warning of impactful research. Nature Biotechnology, 39(10), 1300-1307. https://doi.org/10.1038/s41587-021-00907-6
  59. Wiliam, D. (2003). The impact of educational research on mathematics education. In Second international handbook of mathematics education (pp. 471-490)
  60. Williams, S. R., & Leatham, K. R. (2017). Journal quality in mathematics education. Journal for Research in Mathematics Education, 48(4), 369-396.
  61. Wilson, C. (2001), Informetrics. In: M. E. WILLIAMS, (Ed.), Annual Review of Information Science and Technology, Vol.34, Medford, NJ: Information Today, Inc. for the American Society for Information Science, 3-143.
  62. Yig, K. G. (2022). Research trends in mathematics education: A quantitative content analysis of major journals 2017-2021. J ournal of Pedagogical Research, 6(3), 137-153.
  63. Zhou, Y., Li, Q., Yang, X., & Cheng, H. (2021). Predicting the popularity of scientific publications by an age-based diffusion model. Journal of Informetrics, 15(4), 101177.
  64. Zhu, X., Turney, P., Lemire, D., & Vellino, A. (2015). Measuring academic influence: Not all citations are equal. J ournal of the Association for Information Science and Technology, 66(2), 408-427. https://doi.org/10.1002/asi.23179